Previously, many machine learning approaches have been suggested to classify and detect breast cancer using single-view information [1]}, [2]}, based on texture descriptors or DL networks. Recently, several studies showed that multi-view approaches [3]}, [4]}, [5]} improved the diagnosis of breast cancer. The primary technique behind these approaches is building an end-to-end DL model to classify mammograms' pathology (benign/malignant). This strategy first extracts features from each view independently and then combines four screening mammography views to produce predictions. In addition, we observed that most current work in breast cancer diagnosis focuses on the discrimination of a mammogram exam as malignant or benign. Nevertheless, few methods could classify mammograms into multi-output. This motivates us to build a DL system that is able to provide a multi-label output, including 5 BI-RADS categories (BI-RADS 1\(\div \) 5) and four breast density classes (A\(\div \) D). Our experiments show that the proposed multi-view approach consistently outperforms the single-view approach on internal and external benchmark datasets.
